IntroductionInternational placements challenge students to find the right level of participation, as local practices, language and time pressure may affect their engagement in patient-related tasks or team activities. This study sought to unpack the initiation process during international clinical placements with the ultimate aim to achieve active student participation.MethodsFollowing a constructivist grounded theory approach, we conducted two individual interviews with 15 undergraduate healthcare students (before departure and whilst on placement). To identify emerging themes, we applied an iterative process of data collection and constant comparative analysis. Several team discussions informed further analysis, allowing us to reach a more conceptual level of theory.ResultsFrom our findings we constructed a four-phase model of healthcare students’ initiation of international clinical placements, which brings into focus how the phases of ‘orientation’, ‘adjustment’ and ‘contribution to patient care’ build up towards a ‘sense of belonging’. We identified several factors that induced active student participation in practice, such as a favourable workplace setting, opportunities for learning and a local support network.DiscussionActive student participation is aimed at different goals, depending on the four phases of initiation that eventually lead to a sense of belonging and support workplace learning.
From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.
Mediators generally find mediation of hierarchical workplace conflicts difficult, as it often involves structural power imbalances. This dissertation seeks to increase knowledge of how hierarchical conflict affects how parties and mediators perceive mediation across dyads and across time. Three questions are central to this: (a) How effective in the long-term is the mediation of hierarchical workplace conflicts? (b) How does perceived situational power in supervisor-subordinate dyads relate to mediation effectiveness? (c) Do supervisors and subordinates differ in their emotional experiences during mediation, and are mediators able to perceive these emotions accurately? To answer these questions, we rely on the literature on power, emotions, mediation, and conflict management. We introduce our research via a heuristic model (chapter one). We then present our quantitative empirical research in three chapters based on survey data we collected from supervisors, subordinates, and
